On double-descent in uncertainty quantification in overparametrized models

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In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

2

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Given a sequence of functions

f_1,\ldots,f_n

with

f_i:\mathcal{D}\mapsto \mathbb{R}

, finite-sum minimization seeks a point

{x}^\star \in \mathcal{D}

minimizing

\sum_{j=1}^nf_j(x)/n

. In this work, we propose a key twist into the finite-sum minimizat ...

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The capabilities of deep learning systems have advanced much faster than our ability to understand them. Whilst the gains from deep neural networks (DNNs) are significant, they are accompanied by a growing risk and gravity of a bad outcome. This is troubli ...

On double-descent in uncertainty quantification in overparametrized models

Chattez avec Graph Search

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